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Flood forecasting at Kinabatangan River, Sabah by utilizing Artificial Neural Network (Ann) / Wan Nurulhafizah Bt Abd Razak

By:

Wan Nurulhafizah Bt Abd Razak

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2015Description: x, 37 p. : ill. (some col.) ; 30 cmISBN:

THE0000642(Local)

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Dissertation note: Project Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2015 Abstract: Flood event is among the most influential disaster in Malaysia .Therefore, the developing of flood forecasting model is to minimize the effects of flood and to achieve a model with high accuracy by utilizing Artificial Neural Network (ANN). Artificial Neural Network is a highly non-linear and can capture the complex interactions among input variables in a system without any prior knowledge about the nature of these interactions. Nowadays, ANN is widely used in prediction and forecasting in water resources. The area of study the flood forecasting is carried out at the Balat Station, Kinabatangan River, Sabah where the hourly water level data is collected from Department of Irrigation and Drainage (DID) from year 2000 until 2014. The results indicated that the model develop a highest accuracy is 6 hour time interval for 4000 iteration where the NSC result is 0.996 with lower RMSE 155.341 compared to others iteration and time interval. This modal achieved 100% at allowable error less than 500 mm which is show the prediction of water level. As a conclusion, this model shows high accuracy and water level can be used alone. This can be applied in the real world to give out warning on imminent flood

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Holdings ( 2 )
Title notes ( 4 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	FKASA .N878 2015 r Bc. (Browse shelf(Opens below))	1	Not for loan		0000104291
Final Year Report	UMPLIB GAMBANG	CD 9330 \| FKASA .N878 2015 r Bc. (Browse shelf(Opens below))	1	Not for loan		0000104292

Faculty of Civil Engineering and Earth Resources

Project Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2015

Bibliography : p. 35

Flood event is among the most influential disaster in Malaysia .Therefore, the developing of flood forecasting model is to minimize the effects of flood and to achieve a model with high accuracy by utilizing Artificial Neural Network (ANN). Artificial Neural Network is a highly non-linear and can capture the complex interactions among input variables in a system without any prior knowledge about the nature of these interactions. Nowadays, ANN is widely used in prediction and forecasting in water resources. The area of study the flood forecasting is carried out at the Balat Station, Kinabatangan River, Sabah where the hourly water level data is collected from Department of Irrigation and Drainage (DID) from year 2000 until 2014. The results indicated that the model develop a highest accuracy is 6 hour time interval for 4000 iteration where the NSC result is 0.996 with lower RMSE 155.341 compared to others iteration and time interval. This modal achieved 100% at allowable error less than 500 mm which is show the prediction of water level. As a conclusion, this model shows high accuracy and water level can be used alone. This can be applied in the real world to give out warning on imminent flood